1. Field of the Invention
The present invention is directed to a neural network in which communication between plural processing elements is accomplished simultaneously, and wirelessly, by communicating via electromagnetic waves and, more particularly, to a neural network in which each processing element includes a radio frequency transceiver receiving common carrier outputs from other neural processing elements and transmitting thresholded or non-linearly transformed common carrier outputs to other processing elements via a waveguide which spatially weights the signals between the processing elements.
2. Description of the Related Art
Conventional neural networks communicate to each other via dedicated or shared connections between processing elements. In the dedicated type in which an individual wire connection exists between each processing element, the number of connections and wires grows rapidly as the number of processing elements grow. The number of connections required for a two-layer fully connected network is N.sup.2 where N is the number of processing elements per layer. When a network has a large number of processing elements, such as 1,000, a dedicated connection arrangement, requiring 1,000,000 wires, becomes impractical both physically and economically. One solution to the connection problem is to have the processing elements share one or more buses. However, since each processing element must occupy the bus to which it is connected, during a dedicated time period in each computation cycle, the speed of a system with a large number of processing elements can be very slow.
There are numerous approaches to the neural network processing performed by each processing element (neuron). Most are based on the very simple and conventional algorithm or transfer function, ##EQU1## where Y.sub.j is the output from the j-th processing element, X.sub.i is the output from the i-th neuron, W.sub.ij is the weight of the connection between processing element i and processing element j, and .theta..sub.j is the threshold level for neuron j. This algorithm simply determines that the output from a processing element is to be activated if the weighted summation of the inputs to this processing element is greater than some pre-defined threshold. This algorithm, (and related variants) has been the center of neural research for over forty years because it is a simple mathematical model of what is believed to take place within nervous tissue. Teaching neural networks which have such a conventional algorithm to perform some desired transformation is also well known. What is needed is a neural network connection arrangement that allows large numbers of processing elements to communicate very rapidly with each other and which sufficiently conforms to the traditional way of performing neural processing element operations allowing conventional teaching methods to be used to the best advantage.